The number of travel-acquired dengue infections has been on a constant rise in the United States and Europe over the past decade. An increased volume of international passenger air traffic originating from regions with endemic dengue contributes to the increasing number of dengue cases. This paper reports results from a network-based regression model which uses international passenger travel volumes, travel distances, predictive species distribution models (for the vector species), and infection data to quantify the relative risk of importing travel-acquired dengue infections into the US and Europe from dengue-endemic regions. Given the necessary data, this model can be used to identify optimal locations (origin cities, destination airports, etc.) for dengue surveillance. The model can be extended to other geographical regions and vector-borne diseases, as well as other network-based processes.

Abstract-Contraflow lane reversal-the reversal of lanes in order to temporarily increase the capacity of congested roadscan effectively mitigate traffic congestion during rush hour and emergency evacuation. However, contraflow lane reversal deployed in several cities are designed for specific traffic patterns at specific hours, and do not adapt to fluctuations in actual traffic. Motivated by recent advances in autonomous vehicle technology, we propose a framework for dynamic lane reversal in which the lane directionality is updated quickly and automatically in response to instantaneous traffic conditions recorded by traffic sensors. We analyze the conditions under which dynamic lane reversal is effective and propose an integer linear programming formulation and a bi-level programming formulation to compute the optimal lane reversal configuration that maximizes the traffic flow. In our experiments, active contraflow increases network efficiency by 72%.

Congestion is one of the biggest challenges faced by the transportation community; congestion accounted for an estimated $87.2 billion in losses in 2007 alone. Transportation professionals need to go beyond capacity expansion projects and explore novel strategies to mitigate traffic congestion. Automated intersection management is a novel strategy that has the potential to greatly reduce intersection delay and improve safety. Although the implementation of such a system is contingent on the development of automated vehicles, competitions such as the Grand Challenge and Urban Challenge of the Defense Advanced Research Projects Agency have shown that this technology is feasible and will be available. Therefore, the development of the infrastructure and associated control methods required to exploit fully the benefits of such technology at the system level is critical. This research explores one such innovative strategy, an automated intersection control protocol based on a first-come, first-served (FCFS) reservation system. The FCFS reservation system was shown to reduce intersection delay significantly by exploiting the features of autonomous vehicles. Microscopic simulation experimental results showed that the FCFS reservation system significantly outperformed a traditional traffic signal in reducing delay.

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The objective of this paper is to present a network-based optimization method for identifying links in an air traffic network responsible for carrying infected passengers into previously unexposed regions. The required data include individual infection reports (i.e., when the disease was first reported in a region), travel pattern data, and other geographic properties. The network structure is defined by nodes and links, which represent regions (cities, states, countries) and travel routes, respectively. The proposed methodology is novel in its attempt to replicate an outbreak pattern atop a transportation network by exploiting regional infection data. The problem parallels a related problem in phylodynamics, which uses genetic sequencing data to reconstruct the most likely spatiotemporal path of infection.